Handwriting can reveal the writer's personality traits, which explains the interest in systems for its automated analysis. Such systems require image collection and preprocessing, feature extraction, and personality classification. Feature extraction is usually done by processing raster images. A few publications deal with vectorized handwriting images, but none use the Potrace algorithm and Bezier curves. We propose a modified Potrace algorithm for computing handwriting line spacing, baseline, and slant. The proposed algorithm is applicable for processing skewed texts and can be used to reduce input data complexity for other feature extraction techniques.
In this work, software is developed for modeling the drying process of hygroscopic materials using cellular automata. The modeling process using cellular automata is described, and the developed transition rules that consider heat and mass transfer not only within the material but also between stacks are provided. The developed software uses a modern multi-tier client-server architecture, including a database for handling large volumes of computational data. Additionally, the results of the modeling, including their validation based on existing experimental data, are presented and analyzed.
In today's construction industry, effective communication is critical to project success. Miscommunication and unclear instructions can lead to delays, cost overruns and safety risks. This paper presents some results of implementing a formalized neural network model for communication analysis in a construction company using advanced text analysis techniques such as Word2Vec. The model is aimed at analyzing the effectiveness of the communication of individual employees by determining the key spheres of their influence on communications. This makes it possible to eliminate or prevent the occurrence of destruction in the process of communication.
This research proposes a new noise-resistant approach for predicting water levels using the capacitive sensor output. The method was tested on data from different sensor configurations. The method performance was compared to a neural network-based (NN) model. General NNs are often used for data linearization and noise compensation in smart sensor systems. However, the domain-aware approach is shown to be better suited for usage on embedded systems in terms of accuracy and memory consumption. The testing on datasets from three different sensor configurations shows 1.3x to 7.7x lower RMSE than the NN models. The proposed model also has fewer parameters, requiring 6x to 9x less memory. This result was validated on a real-time Arm Cortex-M0 embedded device.
The work is devoted to the research of methods of optimizing project management in conditions of limited resources. Emphasis was placed on building a hierarchy of tasks and decision-making processes. Strategies for estimating the minimum project duration and costs are presented. Also, with the help of iterative methods, options for management solutions were developed. After analyzing the constraints and formalizing the tasks, practical solutions are provided to improve the efficiency of the project, ensuring time and financial constraints.
